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‎python/Hummingbird_ML.ipynb‎

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "Hummingbird-ML.ipynb",
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"provenance": [],
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"collapsed_sections": []
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"accelerator": "GPU"
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "IpOdlr3WAPHJ",
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"colab_type": "text"
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},
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"source": [
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"# **Hummingbird-ML**\n",
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"\n",
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"[How to Harness GPU to Speed Up Machine Learning with Hummingbird-ML](https://www.youtube.com/watch?v=qN8jcUmo8TI)\n",
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"\n",
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"Adapted from: https://github.com/microsoft/hummingbird"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "ir3DZd5-_jiu",
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"colab_type": "text"
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},
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"source": [
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"# Install Hummingbird-ML"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "ra3JEgWN_bfp",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 408
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},
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"outputId": "4fae39de-26f0-4939-846d-039fb876725a"
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},
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"source": [
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"! pip install hummingbird-ml[extra]"
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],
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Collecting hummingbird-ml[extra]\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/ed/3b/cf1b8c1e7531377adead8de29e29b00b5aed380544ad0def4c0188b50d80/hummingbird_ml-0.0.5-py2.py3-none-any.whl (60kB)\n",
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"\r\u001b[K |█████▌ | 10kB 16.6MB/s eta 0:00:01\r\u001b[K |███████████ | 20kB 1.8MB/s eta 0:00:01\r\u001b[K |████████████████▍ | 30kB 2.2MB/s eta 0:00:01\r\u001b[K |█████████████████████▉ | 40kB 2.5MB/s eta 0:00:01\r\u001b[K |███████████████████████████▎ | 51kB 2.0MB/s eta 0:00:01\r\u001b[K |████████████████████████████████| 61kB 1.8MB/s \n",
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"\u001b[?25hRequirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.6/dist-packages (from hummingbird-ml[extra]) (1.18.5)\n",
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"Requirement already satisfied: torch>=1.4.* in /usr/local/lib/python3.6/dist-packages (from hummingbird-ml[extra]) (1.6.0+cu101)\n",
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"Collecting onnxconverter-common>=1.6.0\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/fe/7a/7e30c643cd7d2ad87689188ef34ce93e657bd14da3605f87bcdbc19cd5b1/onnxconverter_common-1.7.0-py2.py3-none-any.whl (64kB)\n",
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"\u001b[K |████████████████████████████████| 71kB 3.7MB/s \n",
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"\u001b[?25hRequirement already satisfied: scikit-learn>=0.22.1 in /usr/local/lib/python3.6/dist-packages (from hummingbird-ml[extra]) (0.22.2.post1)\n",
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"Requirement already satisfied: xgboost==0.90; extra == \"extra\" in /usr/local/lib/python3.6/dist-packages (from hummingbird-ml[extra]) (0.90)\n",
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"Requirement already satisfied: lightgbm>=2.2; extra == \"extra\" in /usr/local/lib/python3.6/dist-packages (from hummingbird-ml[extra]) (2.2.3)\n",
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"Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch>=1.4.*->hummingbird-ml[extra]) (0.16.0)\n",
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"Collecting onnx\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/36/ee/bc7bc88fc8449266add978627e90c363069211584b937fd867b0ccc59f09/onnx-1.7.0-cp36-cp36m-manylinux1_x86_64.whl (7.4MB)\n",
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"\u001b[K |████████████████████████████████| 7.4MB 16.0MB/s \n",
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"\u001b[?25hRequirement already satisfied: protobuf in /usr/local/lib/python3.6/dist-packages (from onnxconverter-common>=1.6.0->hummingbird-ml[extra]) (3.12.4)\n",
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"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.22.1->hummingbird-ml[extra]) (0.16.0)\n",
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"Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.22.1->hummingbird-ml[extra]) (1.4.1)\n",
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"Requirement already satisfied: typing-extensions>=3.6.2.1 in /usr/local/lib/python3.6/dist-packages (from onnx->onnxconverter-common>=1.6.0->hummingbird-ml[extra]) (3.7.4.3)\n",
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"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from onnx->onnxconverter-common>=1.6.0->hummingbird-ml[extra]) (1.15.0)\n",
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"Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf->onnxconverter-common>=1.6.0->hummingbird-ml[extra]) (49.6.0)\n",
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"Installing collected packages: onnx, onnxconverter-common, hummingbird-ml\n",
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"Successfully installed hummingbird-ml-0.0.5 onnx-1.7.0 onnxconverter-common-1.7.0\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "YnA-PmeA_q70",
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"colab_type": "text"
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},
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"source": [
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"# Import libraries"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "lkIThThi_puf",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"import numpy as np\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from hummingbird.ml import convert"
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],
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"execution_count": 2,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "rFw_4cGa_-tF",
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"colab_type": "text"
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},
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"source": [
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"# Create some random data for binary classification"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "hGGngPPp__mx",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"num_classes = 2\n",
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"X = np.random.rand(100000, 28)\n",
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"y = np.random.randint(num_classes, size=100000)"
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],
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"execution_count": 3,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "WusxNKH4AHII",
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"colab_type": "text"
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},
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"source": [
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"# Create and train a model (scikit-learn RandomForestClassifier)"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "GMRJRuBwAGeV",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"skl_model = RandomForestClassifier(n_estimators=10, max_depth=10)"
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],
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"execution_count": 4,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "M_kGo80yAYTn",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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},
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"outputId": "aa863652-02f8-4578-8fb7-e3b028685cd7"
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},
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"source": [
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"%%timeit\n",
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"skl_model.fit(X, y)"
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],
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"execution_count": 5,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"1 loop, best of 3: 4.78 s per loop\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "Hp4a8I0tAbBl",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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},
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"outputId": "4e083fd5-981f-4238-9158-3f4500585560"
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},
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"source": [
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"%%timeit\n",
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"skl_model.predict(X)"
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],
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"execution_count": 6,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"10 loops, best of 3: 85.6 ms per loop\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "mNiBvy9BA7wR",
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"colab_type": "text"
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},
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"source": [
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"# Use Hummingbird to convert the model to PyTorch"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "vcAOpuxxAzPc",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"model = convert(skl_model, 'pytorch')"
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],
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"execution_count": 7,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "dpt6_4l8BF7e",
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"colab_type": "text"
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},
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"source": [
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"# Run predictions on CPU"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "_BiU63hNBDu-",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 34
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},
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"outputId": "1bd8b158-a62b-4fe0-be09-ca382c817247"
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},
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"source": [
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"%%timeit\n",
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"model.predict(X)"
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],
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"execution_count": 8,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"1 loop, best of 3: 174 ms per loop\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "F10tJEMKBPZG",
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"colab_type": "text"
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},
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"source": [
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"# Run predictions on GPU"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "l2PUbqoHBJBX",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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"model.to('cuda')"
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],
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"execution_count": 9,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "-AB23_VTBRMP",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 51
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},
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"outputId": "b9efea7d-913c-4326-c14a-6b6ca0e9c063"
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},
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"source": [
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"%%timeit\n",
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"model.predict(X)"
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],
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"execution_count": 10,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"The slowest run took 5.22 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
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"100 loops, best of 3: 14.8 ms per loop\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "dbkQU69JDt7T",
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"colab_type": "text"
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},
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"source": [
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"# Calculation Time"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Hr1R_9nwDwpc",
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"colab_type": "text"
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},
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"source": [
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"Methods | Timing | Performance\n",
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"--|--|--\n",
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"scikit-learn | 85.6 ms | -\n",
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"PyTorch (CPU) | 174 ms | 2 X slower than scikit-learn\n",
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"PyTorch (GPU) | 14.8 ms | Almost 6 X faster than scikit-learn; Almost 12 X faster than PyTorch (CPU)"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "9lmR3LHoEzhl",
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"colab_type": "code",
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"colab": {}
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},
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"source": [
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""
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],
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"execution_count": null,
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"outputs": []
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}
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]
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}

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